Object information processing device, object information processing method, and object information processing program

ABSTRACT

Objects are tracked in real time in a composed video acquired by joining a plurality of videos. A grouping candidate determining unit  12  extracts objects present within an overlapping area, in which pieces of frame data are overlapped, among objects that have been detected and tracked in each of a plurality of pieces of frame data that were captured at the same time as candidate objects. A grouping unit  13  arranges a plurality of candidate objects of which a degree of overlapping is equal to or larger than a predetermined threshold as a group, and an integration unit  14  assigns integration object IDs to groups and objects that have not been grouped.

TECHNICAL FIELD

The present invention relates to a technology for tracking an object ina video.

BACKGROUND ART

A technology for composing a panorama video in which an entireappearance of a field game having a wide game space is taken by joiningvideos captured by a plurality of cameras in horizontal/verticaldirections in real time is known. An application to monitoring of aremote place of a large monitoring area by joining videos in real timeusing this technology is being considered.

In monitoring of a remote place using a video, it is preferable todetect and track a monitoring target object from a video in real timeand to be able to display information of the monitoring target objectsuperimposed on the video.

CITATION LIST Non Patent Literature

[Non Patent Literature 1]

-   Yoko ISHII, Tetsuro TOKUNAGA, Yoshihide TONOMURA, and Kota HIDAKA,    “Kirari! Tracker: Review of Real-time Specific Person Tracking    System using LiDAR and Deep Learning Engine”, The Institute of Image    Information and Television Engineers, Winter Annual Convention,    2017, 15B-3

SUMMARY OF THE INVENTION Technical Problem

However, a panorama video acquired by joining a plurality of videos is ahigh-precision video, and thus, there is a problem in that, whendetection and tracking of an object is performed on a panorama video,the process takes time, and the real time property is affected.

The present invention is in consideration of the description presentedabove, and an objective thereof is to track an object in real time in acomposed video acquired by joining a plurality of videos.

Means for Solving the Problem

An object information processing device according to the presentinvention is an object information processing device that tracks objectsin a composed video composed by joining a plurality of pieces of videodata acquired by imaging parts of an imaging area in an overlappingmanner. It includes: a candidate extracting unit configured to extractobjects present within an overlapping area in which the video data isoverlapped among objects detected and tracked in each of the pluralityof pieces of video data as candidate objects; a grouping unit configuredto arrange a plurality of candidate objects of which a degree ofoverlapping is equal to or larger than a predetermined threshold into agroup; and an integration unit configured to assign integration objectIDs to the group and the objects that have not been grouped.

An object information processing method according to the presentinvention is an object information processing method using an objectinformation processing device that tracks objects in a composed videocomposed by joining a plurality of pieces of video data acquired byimaging parts of an imaging area in an overlapping manner. It includes:extracting objects present within an overlapping area in which the videodata is overlapped among objects detected and tracked in each of theplurality of pieces of video data as candidate objects; arranging aplurality of candidate objects of which a degree of overlapping is equalto or larger than a predetermined threshold into a group; and assigningintegration object IDs to the group and the objects that have not beengrouped.

An object information processing program according to the presentinvention operates a computer as each of the units of the objectinformation processing device described above.

Effects of the Invention

According to the present invention, an object can be tracked in realtime in a composed video acquired by joining a plurality of videos.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating the configuration of a wide viewingangle remote monitoring system configured using an object informationprocessing device according to an embodiment.

FIG. 2(a) is a diagram illustrating an aspect in which objects aredetected from each of the input videos input by the wide viewing angleremote monitoring system.

FIG. 2(b) is a diagram illustrating an aspect in which a plurality ofvideos are joined.

FIG. 2(c) is a diagram illustrating an example of display of a panoramavideo output by the wide viewing angle remote monitoring system.

FIG. 3 is a functional block diagram illustrating the configuration ofan object information processing device according to the embodiment.

FIG. 4 is a flowchart illustrating the flow of object informationprocessing according to the embodiment.

FIG. 5 is a diagram illustrating an example of a panorama video acquiredby joining frame data and detected objects.

FIG. 6 is a flowchart illustrating the flow of a grouping process.

FIG. 7 is a diagram illustrating an example in which grouping is notperformed.

FIG. 8(a) is a diagram illustrating an example in which the same objectis detected in a plurality of overlapping areas.

FIG. 8(b) is a diagram illustrating an example of regrouping groupsincluding the same object detected in a plurality of overlapping areas.

FIG. 9 is a diagram illustrating an example in which a group or anobject taking over an integration object ID cannot be identified.

FIG. 10 is a flowchart illustrating the flow of a process of assigningan integration object ID.

FIG. 11 is a diagram illustrating an example of assigning an integrationobject ID.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment of the present invention will be describedwith reference to the drawings. In description of the drawings below,the same or similar components are designated by the same or similarreference signs.

The configuration of a wide viewing angle remote monitoring system usingobject information processing according to the present invention will bedescribed with reference to FIG. 1. The wide viewing angle remotemonitoring system illustrated in the drawing is a system that realizeslow latency processing of generating a panorama video acquired bycomposing a plurality of videos, acquiring object information on amonitoring target object from the video, and synchronously transmittingthe panorama video, audio, and object information through an IP networkusing an MPEG Media Transport Protocol (MMTP). For example, the wideviewing angle remote monitoring system can be used for wide viewingangle monitoring such as air traffic control, public space monitoring,disaster monitoring, and the like.

The wide viewing angle remote monitoring system illustrated in FIG. 1includes a composition processing server 100, an object informationintegrating server 200, a decode server 300, and an integrated objectinformation receiving server 400.

The composition processing server 100 includes a composition processingunit 110, an encoding processing unit 120, and an objectdetection/tracking processing unit 130. The composition processingserver 100 receives a video and audio from each of a plurality ofimaging systems (for example, 4K cameras) as inputs, performs detectionand tracking of a target object from each video (FIG. 2(a)), andcomposes a panorama video by joining the videos (FIG. 2(b)).

The composition processing unit 110 composes a panorama video by joininga plurality of input videos in real time. The composition processingunit 110 may dynamically change seams at which the videos are stitchedor may statically set seams in advance using a setting file or the like.

The encoding processing unit 120 encodes the panorama video composed bythe composition processing unit 110 and audio data to convert theresultant of encoding into an MMTP stream, and transmits the MMTP streamto the decode server 300.

The object detection/tracking processing unit 130 performs detection andtracking of a target object from each video. The objectdetection/tracking processing unit 130 transmits a result of tracking ofan object in each video to the composition processing unit 110 and alsotransmits the result to the object information integrating server 200.

For an object that has been detected and tracked in each video by theobject detection/tracking processing unit 130, the object informationintegrating server 200 converts coordinates of the object in each videointo coordinates in a panorama video. In a case where an object isdetected in a video before composition, in an overlapping area in whichvideos overlap, there are cases in which the same object is detected ineach of the overlapping videos. The object information integratingserver 200 integrates tracking results of objects that are estimated tobe the same among objects detected in the overlapping area using objectinformation processing according to the present invention. Details ofthe object information processing according to the present inventionwill be described below.

The decode server 300 decodes an MMTP stream received from thecomposition processing server 100 and outputs a panorama video andaudio.

The integrated object information receiving server 400 receives MMTPpackets of object information from the object information integratingserver 200 and outputs the object information.

A display system (for example, a panorama screen) displays the objectinformation output from the integrated object information receivingserver 400 superimposed on the panorama video output from the decodeserver 300 (FIG. 2(c)). At this time, the display system superimposesobject information having a time stamp such as a time stamp attached toframe data of the panorama video.

Configuration of Object Information Processing Device

The configuration of the object information processing device 1according to the embodiment will be described with reference to FIG. 3.The object information processing device 1 is a device that outputsresults of tracking of objects in a panorama video composed by a videoprocessing device 3. More specifically, the object informationprocessing device 1 receives a tracking result of an object in eachvideo from the video processing device 3, converts coordinates of theobject in each video (hereinafter referred to as “local coordinates”)into coordinates in the panorama video (hereinafter referred to as“global coordinates”), and integrates tracking results of objectsregarded as being the same in an overlapping area. The objectinformation processing device 1 corresponds to the object informationintegrating server 200 of the wide viewing angle remote monitoringsystem illustrated in FIG. 1, and the video processing device 3corresponds to the composition processing server 100.

The object information processing device 1 illustrated in FIG. 3includes a tracking result receiving unit 11, a grouping candidatedetermining unit 12, a grouping unit 13, and an integration unit 14.Each of the units included in the object information processing device 1may be configured by a computer including an operation processingdevice, a storage device, and the like, and a process of each of theunits may be executed using a program. This program is stored in astorage device included in the object information processing device 1and may be recorded on a recording medium such as a magnetic disk, anoptical disc, a semiconductor memory, or the like and may be providedthrough a network.

The tracking result receiving unit 11 receives a tracking result of anobject in each video and converts local coordinates of the object intoglobal coordinates. The tracking result includes local coordinates and alocal object ID of the object. The tracking result may include a name, adegree of reliability (correctness of a name of the object), and a colorof the detected object and an acceleration and a movement direction ofthe object. The local object ID is an identifier that is assigned to anobject that is being tracked in each video. The same local object ID isassigned to an object that is determined to be the same as an objectdetected in a previous generation (past frame). A local object ID isassigned to an object for each video, and thus, in a case where shownvideos of the same objects are different from each other, differentlocal object IDs are assigned to the objects of the videos.

The grouping candidate determining unit 12 extracts objects detectedwithin an overlapping area as integration targets. The coordinates of anoverlapping area have been calculated in advance. The object informationprocessing device 1 may be configured to receive coordinates of anoverlapping area from the video processing device 3.

The grouping unit 13 performs grouping by estimating objects, of which adegree of overlapping between objects is high, detected in overlappingvideos in an overlapping area as the same objects. In addition, thegrouping unit 13 performs re-grouping by arranging groups togetherincluding objects to which the same local object ID is assigned inoverlapping areas (different overlapping areas within one video) thatare adjacent to each other.

The integration unit 14 assigns an integration object ID that is atracking result on a panorama video to each of groups and objects thatare not grouped. By using this integration object ID, the same objectcan be continuously tracked in a panorama video.

Object Information Processing

Next, object information processing according to the embodiment will bedescribed with reference to FIGS. 4 and 5.

In Step S1, the video processing device 3 receives videos from aplurality of cameras as inputs and acquires frame data for the same timefrom each of the input videos.

In Step S2, the video processing device 3 detects an object from eachvideo and tracks the object in each video. In order to increase thespeed of the object detection process, the video processing device 3 maydetect an object using data acquired by reducing the size of frame dataacquired from each video. The detection and tracking of an object may beperformed in parallel for each video. The tracking is tracking of themovement of an object by determining the similarity between an objectdetected in current frame data and an object detected in past framedata.

The video processing device 3 composes a panorama video by overlappingframe data F1 and F2 such that feature points of the frame data F1 andF2 of the same time adjacent to each other coincide with each other.FIG. 5 illustrates an aspect in which horizontally adjacent frame dataF1 and F2 overlaps. An area in which the frame data F1 and F2 overlapsis an overlapping area. An area in which data does not overlap will bereferred to as a non-overlapping area. A panorama video may be composedby overlapping pieces of frame data that are vertically adjacent to eachother, and a panorama video may be composed by overlapping frame datathat is aligned vertically and horizontally.

In addition, the video processing device 3 detects an object from eachvideo before composition of a panorama video and tracks the object. Inthe example illustrated in FIG. 5, the video processing device 3 detectssix objects O11 to O16 from the frame data F1 and detects six objectsO21 to O26 from the frame data F2. A tracking result of objects in eachvideo is transmitted to the object information processing device 1.

In Step S3, the object information processing device 1 receives atracking result of objects in each video and converts local coordinatesof each object into global coordinates.

In Step S4, the object information processing device 1 determineswhether or not objects are present within an overlapping area. Theobjects present within an overlapping area are candidates for grouping.In the example illustrated in FIG. 5, objects O14, O15, and O16 detectedin the frame data F1 are present within the overlapping area, andobjects O21, O22, and O23 detected in the frame data F2 are presentwithin the overlapping area.

In Step S5, the object information processing device 1 groups localobjects, which simultaneously appear in a plurality of videos, and areestimated to be the same object. The object information processingdevice 1 estimates objects, of which a degree of overlapping is equal toor larger than a predetermined threshold among objects detected in anoverlapping area of pieces of frame data that are adjacent to eachother, to be the same object and groups the estimated objects. In theexample illustrated in FIG. 5, the object O14 detected in the frame dataF1 and the object O21 detected in the frame data F2 are grouped into agroup G1, and the object O16 detected in the frame data F1 and theobject O23 detected in the frame data F2 are grouped into a group G2.Details of the grouping process will be described below.

In Step S6, the object information processing device 1 assignsintegration object IDs to groups acquired by grouping objects and eachobject that has not been grouped. In the example illustrated in FIG. 5,numbers written below groups and objects are assigned integration objectIDs. More specifically, integration object IDs of the groups G1 and G2are respectively “0004” and “0007.” Integration object IDs of objectsO11, O12, O13, and O15 that have not been grouped are respectively“0001,” “0002,” “0003,” and “0005.” Integration object IDs of objectsO22, O24, O25, and O26 that have not been grouped are respectively“0006,” “0008,” “0009,” and “0010.” Details of the process of assigningintegration object IDs will be described below.

Grouping Next, a grouping process will be described with reference toFIG. 6. The grouping process is performed on objects detected within anoverlapping area.

In Step S51, the grouping unit 13 extracts sets of objects of which adegree of overlapping between objects detected in each of pieces offrame data adjacent to each other is equal to or larger than athreshold. For a certain object, in a case where there is no object ofwhich a degree of overlapping therewith is equal to or larger than thethreshold in adjacent frame data, the object is not a grouping target.The number of objects to be extracted may be changed in accordance withthe number of pieces of frame data overlapped in an overlapping area.For example, in a case where frame data overlaps and is alignedvertically and horizontally, four pieces of frame data may overlap ateach corner part of the frame data. In such a case, the grouping unit 13may extract four objects as grouping targets.

In Step S52, the grouping unit 13 sets a set, in which an integrationobject ID has been established for any object in a non-overlapping areaamong sets of objects of which a degree of overlapping is equal to orlarger than the threshold, as a non-grouping target. For example, asillustrated in FIG. 7, in a past generation, it is assumed that anintegration object ID “0001” has been assigned to the object O11 in anon-overlapping area, and an integration object ID “0002” has beenassigned to the object O21 in a non-overlapping area. In other words, inthe past generation, the objects O11 and O21 are recognized as separateobjects. In a current generation, even when a degree of overlappingbetween the objects O11 and O21 is equal to or larger than thethreshold, for the objects O11 and O21, integration object IDs areestablished in a non-overlapping area, and thus the objects O11 and O21are separate objects. Thus, the grouping unit 13 sets a set of theobjects O11 and O21 as a non-grouping target.

For example, when an integration object ID is assigned to an object inan overlapping area, the integration unit 14 sets a flag of the objectto on. The grouping unit 13 sets a set of objects of which all the flagsare on as a non-grouping target. A set of objects in which a flag of oneobject is on and a flag of the other object is off is a grouping target.

As a method for setting a non-grouping target, in a case where a namerepresenting each detected object is estimated, the grouping unit 13 mayset a set of objects of which names are different from each other as anon-grouping target. For example, a set of an object estimated as aperson and an object estimated as a signal is set as a non-groupingtarget.

In addition, in a case where colors of objects are clearly differentfrom each other, the grouping unit 13 may set a set of the objects as anon-grouping target.

Furthermore, in a case where movement directions of objects areperceived, and objects are moving in different directions, the groupingunit 13 may set a set of the objects as a non-grouping target.

In Step S53, the grouping unit 13 groups sets that have not beenexcluded in Step S52 among sets of objects of which a degree ofoverlapping between the objects is equal to or larger than thethreshold.

In the following Steps S54 and S55, the grouping unit 13 may performregrouping by arranging groups including the same objects together.

In Step S54, the grouping unit 13 determines whether or not a groupincluding the same object is present in overlapping areas adjacent toeach other. Overlapping areas adjacent to each other are a plurality ofoverlapping areas in data of one frame. For example, in the exampleillustrated in FIG. 8(a), overlapping areas disposed on the left sideand the right side of the frame data F2 are overlapping areas adjacentto each other. Whether or not objects are the same can be determinedbased on whether or not local object IDs are the same.

In the example illustrated in FIG. 8(a), an object O11 detected in framedata F1 and an object O21 detected in frame data F2 are grouped as agroup G1. In addition, an object O31 detected in frame data F3 and anobject O21 detected in frame data F2 are grouped as a group G2. Thegroups G1 and G2 include the same object O21.

In Step S55, the grouping unit 13 performs re-grouping by arranging thegroups including the same object together. In the example illustrated inFIG. 8(b), the groups G1 and G2 including the same object O21 areregrouped as a group G3.

Assignment of Integration Object ID Next, a process of assigningintegration object IDs will be described.

For example, as illustrated in FIG. 9, in a case where grouped objectschange, a group taking over an integration object ID assigned to a groupin the previous generation cannot be identified. In the exampleillustrated in FIG. 9, in the previous generation, an integration objectID of an object O11 is “0001,” an integration object ID of a group G1including objects O12 and O21 is “0002,” and an integration object ID ofa group G2 including objects O13 and O22 is “0003.” In a currentgeneration, it is assumed that a set of the objects O11 and O21 has beengrouped into a group G3, a set of objects O12 and O22 has been groupedinto a group G4, and the object O13 has not been grouped.

In the example illustrated in FIG. 9, in the current generation, it isunclear whether the integration object ID “0003” of the group G2including the objects O13 and O22 should be taken over by the group G4including the object O22 or should be taken over by the object O13. Inaddition, it is unclear whether the group G3 should take over theintegration object ID “0001” of the object O11 of the previousgeneration or should take over the integration object ID “0002” of thegroup G1 including the object O21.

Thus, in the embodiment, a time (a survival duration) in which eachobject is tracked in each video is managed, and integration object IDsare assigned to objects in order of longest to shortest survivaldurations of the objects. For example, in the example illustrated inFIG. 9, if a survival duration of the object O13 is longer than asurvival duration of the object O22, the process is performed from theobject O13, and the object O13 takes over the integration object ID“0003” of the group G2. At this time, the processing order of the objectO22 having no integration object ID to be taken over is postponed. Forexample, the processing order of the object O22 is lowered to be in thesame level as that of an object that is newly detected. The object O22is handled as an object to which an integration object ID has not beenassigned in the previous generation.

In the example illustrated in FIG. 9, it is unclear whether or not thegroup G3 should take over one of the integration object ID “0001” or“0002.” In the embodiment, in a case where a plurality of integrationobject IDs can be taken over, an integration object ID having thelongest survival duration is taken over. An integration object ID havingthe longest survival duration is an integration object ID that has beendelivered at an earlier time. More specifically, if the survivalduration of the integration object ID “0001” is longer than the survivalduration of the integration object ID “0002,” the group G3 takes overthe integration object ID “0001.”

The process of assigning integration object IDs will be described withreference to FIG. 10. The process illustrated in FIG. 10 is performed onall the groups and all the objects that have not been grouped.

In Step S61, the integration unit 14 selects an object for which thesurvival duration is the longest or a group including an object forwhich the survival duration is the longest.

In Step S62, the integration unit 14 determines whether or not anintegration object ID has been assigned to an object or a group that isa processing target in the previous generation. In a case where theprocessing target is a group, it is determined whether or not anintegration object ID has been assigned to at least any one of objectsincluded in the group.

In accordance with a determination that an integration object ID has notbeen assigned in the previous generation, the integration unit 14 newlyassigns an integration object ID to the object or the group that is theprocessing target in Step S63.

In Step S64, the integration unit 14 determines whether or not the groupthat is the processing target includes a plurality of objects to whichdifferent integration object IDs have been assigned.

In accordance with a determination that the processing target is anobject or in a case where a group that is the processing target does notinclude a plurality of objects to which different integration object IDsare assigned, in other words, in a case where an integration object IDto be taken over is set to one, the integration unit 14 causes theobject or the group that is the processing target to take over theintegration object ID of the previous generation in Step S65.

In accordance with a determination that the group that is the processingtarget includes a plurality of objects to which different integrationobject IDs have been assigned, the integration unit 14 causes the groupthat is the processing target to take over an integration object ID ofwhich the survival duration is the longest in Step S66.

The integration unit 14 performs the process described above on all thegroups and all the objects.

An example of assignment of integration object IDs will be describedwith reference to FIG. 11.

In the example illustrated in FIG. 11, in a previous generation (oneframe before), five objects O11, O12, O13, O14, and O15 have beendetected from one piece of frame data, and five objects O21, O22, O23,O24, and O25 have been detected from the other piece of frame data. Theobjects O14 and O21 are grouped into a group G1, and the objects O15 andO22 are grouped into a group G2. Integration object IDs “0001,” “0002”,and “0003” have been respectively assigned to the objects O11, O12, andO13. Integration object IDs “0004” and “0005” have been respectivelyassigned to the groups G1 and G2. Integration object IDs “0006,” “0007,”and “0008” have been respectively assigned to the objects O23, O24, andO25.

In a current generation (latest frame), an object O16 has been newlydetected from one piece of frame data. Two objects O26 and O27 have beennewly detected from the other piece of frame data. The object O25 thathas been detected in the previous generation is not detected in thecurrent generation.

The object O26 that has been newly detected and the object O13 aregrouped into a group G3. The object O15 and the object O21 are groupedinto a group G4. The object O14 and the object O22 have not beengrouped.

The objects O11, O12, O23, and O24 that have not been grouped in theprevious generation and have not been grouped also in the currentgeneration take over integration object IDs of the previous generation.

New integration object IDs “0009” and “0011” are respectively deliveredto the objects O16 and O27 that have not been grouped and have beennewly detected.

The integration object ID “0008” that has been assigned to the objectO25 for which the current generation has not been detected is deleted.

Processes for the objects O14, O15, O21, and O22 that have been groupedin the previous generation and the objects O13 and O26 that have beengrouped in the current generation will be considered. Here, it isassumed that the survival duration of an object is longer in order ofthe objects O13, O14, O15, O21, O22, and O26.

First, a group G3 including the object O13 is a processing target. Anintegration object ID “0003” has been assigned to the object O13 in theprevious generation. An object O26 included in the group G3 is anewly-detected object, and an integration object ID has not beenassigned thereto. Thus, the group G3 takes over the integration objectID “0003” of the object O13.

Subsequently, the object O14 becomes the processing target. The objectO14 is included in the group G1 in the previous generation. Anintegration object ID “0004” is assigned to the group G1. Thus, theobject O14 takes over the integration object ID “0004” of the group G1.

The object O21 included in the group G1 in the previous generation ishandled as an object to which an integration object ID has not beenassigned in the previous generation, and the order of the processthereof is lowered.

Subsequently, the group G4 including the object O15 becomes theprocessing target. The object O15 has been included in the group G2 inthe previous generation. An integration object ID “0005” is assigned tothe group G2. Although the object O21 has been included in the group G1in the previous generation, it becomes an object to which an integrationobject ID has not been assigned at the time of processing the objectO14. Thus, the group G4 takes over the integration object ID “0005” ofthe group G2.

The object O22 included in the group G2 in the previous generation ishandled as an object to which an integration object ID has not beenassigned in the previous generation, and the order of the processthereof is lowered.

Finally, the object O22 becomes the processing target. Although theobject O22 has been included in the group G2 in the previous generation,it becomes an object to which an integration object ID has not beenassigned at the time of processing the object O15. Thus, a newintegration object ID “0010” is delivered to the object O22.

According to the process described above, the integration unit 14 cancause integration object IDs to be appropriately taken over.

As described above, according to the embodiment, the grouping candidatedetermining unit 12 extracts objects present within an overlapping area,in which pieces of frame data are overlapped, among objects that havebeen detected and tracked in each of a plurality of pieces of frame datathat were captured at the same time as candidate objects. The groupingunit 13 arranges a plurality of candidate objects of which a degree ofoverlapping is equal to or larger than a predetermined threshold as agroup and the integration unit 14 assigns integration object IDs togroups and objects that have not been grouped, whereby object trackingprocesses can be performed in parallel in individual videos in a casewhere the videos are composed. Thus, a processing time of the trackingcan be shortened, and the object information processing according to theembodiment can be performed in parallel with a video compositionprocess. As a result, an object can be tracked in real time in acomposed video.

REFERENCE SIGNS LIST

-   1 Object information processing device-   11 Tracking result receiving unit-   12 Grouping candidate determining unit-   13 Grouping unit-   14 Integration unit-   3 Video processing device-   100 Composition processing server-   110 Composition processing unit-   120 Encoding processing unit-   130 Object detection/tracking processing unit-   200 Object information integrating server-   300 Decode server-   400 Integrated object information receiving server

1. An object information processing device that tracks objects in acomposed video composed by joining a plurality of pieces of video dataacquired by imaging parts of an imaging area in an overlapping manner,the object information processing device comprising: a candidateextracting unit configured to extract objects present within anoverlapping area in which video data is overlapped among objectsdetected and tracked in each of the plurality of pieces of video data ascandidate objects; a grouping unit configured to arrange a plurality ofcandidate objects of which a degree of overlapping is equal to or largerthan a predetermined threshold into a group; and an integration unitconfigured to assign integration object IDs to the group and the objectsthat have not been grouped.
 2. The object information processing deviceaccording to claim 1, wherein, in a case where groups including the samecandidate object are present in each of a plurality of overlapping areaswithin the same video data, the grouping unit arranges a plurality ofthe groups into one group.
 3. The object information processing deviceaccording to claim 1, wherein, in a case where a plurality of candidateobjects of which a degree of overlapping is equal to or larger than apredetermined threshold are recognized as independent objects, thegrouping unit does not arrange the plurality of objects into a group. 4.The object information processing device according to claim 3, wherein,in a case in which the integration object IDs are established for theplurality of candidate objects in a non-overlapping area other than theoverlapping areas, the grouping unit does not arrange the plurality ofcandidate objects into a group.
 5. The object information processingdevice according to claim 1, wherein the integration unit assigns theintegration object IDs in order from the group including the object forwhich a survival duration is the longest or from the object for whichthe survival duration is the longest.
 6. An object informationprocessing method using an object information processing device thattracks objects in a composed video composed by joining a plurality ofpieces of video data acquired by imaging parts of an imaging area in anoverlapping manner, the object information processing method comprising:extracting objects present within an overlapping area in which videodata is overlapped among objects detected and tracked in each of theplurality of pieces of video data as candidate objects; arranging aplurality of candidate objects of which a degree of overlapping is equalto or larger than a predetermined threshold into a group; and assigningintegration object IDs to the group and the objects that have not beengrouped.
 7. A non-transitory computer readable storage medium havingstored thereon an object information processing program for causing acomputer to operate as each of the units of the object informationprocessing device according to claim 1.